https://github.com/ikegami-yukino/oll-python
Online machine learning algorithms (based on OLL C++ library)
https://github.com/ikegami-yukino/oll-python
alma binary-classification machine-learning online-learning perceptron
Last synced: 3 months ago
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Online machine learning algorithms (based on OLL C++ library)
- Host: GitHub
- URL: https://github.com/ikegami-yukino/oll-python
- Owner: ikegami-yukino
- License: bsd-3-clause
- Created: 2013-10-10T18:26:45.000Z (almost 12 years ago)
- Default Branch: master
- Last Pushed: 2017-06-29T17:00:17.000Z (over 8 years ago)
- Last Synced: 2025-06-23T08:03:17.856Z (4 months ago)
- Topics: alma, binary-classification, machine-learning, online-learning, perceptron
- Language: C++
- Homepage:
- Size: 96.7 KB
- Stars: 22
- Watchers: 3
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES.rst
- License: LICENSE
Awesome Lists containing this project
README
oll-python
==========
|travis| |coveralls| |version| |license|
This is a Python binding of the OLL library for machine learning.
Currently, OLL 0.03 supports following binary classification algorithms:
- Perceptron
- Averaged Perceptron
- Passive Agressive (PA, PA-I, PA-II, Kernelized)
- ALMA (modified slightly from original)
- Confidence Weighted Linear-Classification.
For details of oll, see: http://code.google.com/p/oll
Installation
------------
::
$ pip install oll
OLL library is bundled, so you don't need to install it separately.
Usage
-----
.. code:: python
import oll
# You can choose algorithms in
# "P" -> Perceptron,
# "AP" -> Averaged Perceptron,
# "PA" -> Passive Agressive,
# "PA1" -> Passive Agressive-I,
# "PA2" -> Passive Agressive-II,
# "PAK" -> Kernelized Passive Agressive,
# "CW" -> Confidence Weighted Linear-Classification,
# "AL" -> ALMA
o = oll.oll("CW", C=1.0, bias=0.0)
o.add({0: 1.0, 1: 2.0, 2: -1.0}, 1) # train
o.classify({0:1.0, 1:1.0}) # predict
o.save('oll.model')
o.load('oll.model')
# scikit-learn like fit/predict interface
import numpy as np
array = np.array([[1, 2, -1], [0, 0, 1]])
o.fit(array, [1, -1])
o.predict(np.array([[1, 2, -1], [0, 0, 1]]))
# => [1, -1]
from scipy.sparse import csr_matrix
matrix = csr_matrix([[1, 2, -1], [0, 0, 1]])
o.fit(matrix, [1, -1])
o.predict(matrix)
# => [1, -1]
# Multi label classification
import time
import oll
from sklearn.multiclass import OutputCodeClassifier
from sklearn import datasets, cross_validation, metrics
dataset = datasets.load_digits()
ALGORITHMS = ("P", "AP", "PA", "PA1", "PA2", "PAK", "CW", "AL")
for algorithm in ALGORITHMS:
print(algorithm)
occ_predicts = []
expected = []
start = time.time()
for (train_idx, test_idx) in cross_validation.StratifiedKFold(dataset.target,
n_folds=10, shuffle=True):
clf = OutputCodeClassifier(oll.oll(algorithm))
clf.fit(dataset.data[train_idx], dataset.target[train_idx])
occ_predicts += list(clf.predict(dataset.data[test_idx]))
expected += list(dataset.target[test_idx])
print('Elapsed time: %s' % (time.time() - start))
print('Accuracy', metrics.accuracy_score(expected, occ_predicts))
# => P
# => Elapsed time: 109.82188701629639
# => Accuracy 0.770172509738
# => AP
# => Elapsed time: 111.42936396598816
# => Accuracy 0.760155815248
# => PA
# => Elapsed time: 110.95964503288269
# => Accuracy 0.74735670562
# => PA1
# => Elapsed time: 111.39844799041748
# => Accuracy 0.806343906511
# => PA2
# => Elapsed time: 115.12716913223267
# => Accuracy 0.766277128548
# => PAK
# => Elapsed time: 119.53838682174683
# => Accuracy 0.77796327212
# => CW
# => Elapsed time: 121.20785689353943
# => Accuracy 0.771285475793
# => AL
# => Elapsed time: 116.52497220039368
# => Accuracy 0.785754034502
Note
----
- This module requires C++ compiler to build.
- oll.cpp & oll.hpp : Copyright (c) 2011, Daisuke Okanohara
- oll_swig_wrap.cxx is generated based on 'oll_swig.i' in oll-ruby (https://github.com/syou6162/oll-ruby)
License
-------
New BSD License.
.. |travis| image:: https://travis-ci.org/ikegami-yukino/oll-python.svg?branch=master
:target: https://travis-ci.org/ikegami-yukino/oll-python
:alt: travis-ci.org
.. |coveralls| image:: https://coveralls.io/repos/ikegami-yukino/oll-python/badge.png
:target: https://coveralls.io/r/ikegami-yukino/oll-python
:alt: coveralls.io
.. |version| image:: https://img.shields.io/pypi/v/oll.svg
:target: http://pypi.python.org/pypi/oll/
:alt: latest version
.. |license| image:: https://img.shields.io/pypi/l/oll.svg
:target: http://pypi.python.org/pypi/oll/
:alt: license